Following [1]} ganea2017deep, we instantiate the local model as an attention model based on pre-trained word and entity embeddings. Specifically, for each mention \(m_i\) , a pruned candidate set \(C_i=(e_{i1},...,e_{il_i})\)  is identified in the candidate generation stage. We compute a local context score for each \(e \in C_i\)  based on the K-word (in practice, K is set to 100 and stop words are removed.) local context \(c=\lbrace w_1,...,w_K\rbrace \)  surrounding \(m_i\) .
\(\Psi _{\rm long}(e,c) = {\mathbf {x}}_{e}^\top {\mathbf {B}} \, h(c)\) 
